Towards privacy-preserving conversation analysis in everyday life: Exploring the privacy-utility trade-off

Recordings in everyday life provide valuable insights for health-related applications, such as analyzing conversational behavior as an indicator of social interaction and well-being. However, these recordings require privacy preservation of both the speech content and the speaker's identity of all persons involved. This article investigates privacy-preserving features feasible for power-constrained recording devices by combining smoothing and subsampling in the frequency and time domain with a low-cost speaker anonymization technique. A speech recognition and a speaker verification system are used to evaluate privacy protection, whereas a voice activity detection and a speaker diarization model are used to assess the utility for analyzing conversations. The evaluation results demonstrate that combining speaker anonymization with the aforementioned smoothing and subsampling protects speech privacy, albeit at the expense of utility performance. Overall, our privacy-preserving methods offer various trade-offs between privacy and utility, reflecting the requirements of different application scenarios.